Time series forecasting plays a crucial role in diverse fields, necessitating
the development of robust models that can effectively handle complex temporal
patterns. In this article, we present a novel feature selection method embedded
in Long Short-Term Memory networks, leveraging a multi-objective evolutionary
algorithm. Our approach optimizes the weights and biases of the LSTM in a
partitioned manner, with each objective function of the evolutionary algorithm
targeting the root mean square error in a specific data partition. The set of
non-dominated forecast models identified by the algorithm is then utilized to
construct a meta-model through stacking-based ensemble learning. Furthermore,
our proposed method provides an avenue for attribute importance determination,
as the frequency of selection for each attribute in the set of non-dominated
forecasting models reflects their significance. This attribute importance
insight adds an interpretable dimension to the forecasting process.
Experimental evaluations on air quality time series data from Italy and
southeast Spain demonstrate that our method substantially improves the
generalization ability of conventional LSTMs, effectively reducing overfitting.
Comparative analyses against state-of-the-art CancelOut and EAR-FS methods
highlight the superior performance of our approach.

LSTM 网络在多目标进化算法的支持下，通过分区优化权重和偏差，构建非优势预测模型集合进行元模型构建，并提供属性重要性确定的方法，实验证明优于现有方法。